Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) signifies a radical shift in Earth Sciences, enhancing our capacity to identify, evaluate, and understand intricate planetary phenomena. This investigation examines the significant influence of AI and IoT technologies on environmental monitoring, data processing, and scientific comprehension. Contemporary machine learning methodologies, intricate sensor networks, and cutting-edge data acquisition technology empower researchers to amass, analyse, and interpret environmental data with unparalleled accuracy and magnitude. IoT devices produce continuous, detailed data streams from oceanic, terrestrial, and atmospheric settings, which AI algorithms transform into actionable insights, predictive models, and extensive scientific comprehension. Data preparation, model customisation, community distribution, and operational maintenance are needed to apply AI to Earth sciences. Machine Learning Operations (MLOps) frameworks enable AI model generation, deployment, and improvement. This study shows how AI in Earth Sciences requires computer scientists, mathematicians, and geologists to collaborate. The future of environmental research depends on powerful, flexible AI tools that can uncover new patterns, accelerate scientific advances, and provide deeper insights into planetary dynamics.